In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques
Kakao Corporation（Kakao 公司）
我们提出了一种有效的方法来预测搜索查询项目的关系。我们结合了预训练的Transformer和LSTM模型，并使用对抗训练、指数移动平均、多采样丢弃和基于多样性的集成来提高模型的鲁棒性，以解决一个非常困难的问题，即针对以前从未见过的查询进行预测。我们所有的策略都专注于提高深度学习模型的鲁棒性，并且适用于使用深度学习模型的任何任务。应用我们的策略，我们在KDD Cup 2022产品替代分类任务中获得第10名。
PankRAG: Enhancing Graph Retrieval via Globally Aware Query Resolution and Dependency-Aware Reranking Mechanism
College of Computer Science, Beijing university of technology, Beijing, China（北京理工大学计算机科学学院）；Department of Computer Science and Engineering, Bologna University, Bologna, Italy（博洛尼亚大学计算机科学与工程系）
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；School of Atmospheric Sciences, Sun Yat-sen University（中山大学大气科学学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；State Key Laboratory of Intelligent Game, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所智能游戏国家重点实验室）；Pazhou Lab（琶洲实验室）
